The goal of this evaluation was to determine how museum visitors responded to the museum's existing live animal exhibits and identify recommendations for their new Live Animal Garden exhibit.
Recent advances in multimodal learning analytics show significant promise for addressing these challenges by combining multi-channel data streams from fully-instrumented exhibit spaces with multimodal machine learning techniques to model patterns in visitor experience data. We describe initial work on the creation of a multimodal learning analytics framework for investigating visitor engagement with a game-based interactive surface exhibit for science museums called Future Worlds.
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TEAM MEMBERS:
Jonathan RoweWookhee MinSeung LeeBradford MottJames Lester
resourceresearchMuseum and Science Center Exhibits
Multimodal models often utilize video data to capture learner behavior, but video cameras are not always feasible, or even desirable, to use in museums. To address this issue while still harnessing the predictive capacities of multimodal models, we investigate adversarial discriminative domain adaptation for generating modality-invariant representations of both unimodal and multimodal data captured from museum visitors as they engage with interactive science museum exhibits.
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TEAM MEMBERS:
Nathan HendersonWookhee MinAndrew EmersonJonathan RoweSeung LeeJames MinogueJames Lester
resourceresearchMuseum and Science Center Exhibits
Recent years have seen a growing interest in investigating visitor engagement in science museums with multimodal learning analytics. Visitor engagement is a multidimensional process that unfolds temporally over the course of a museum visit. In this paper, we introduce a multimodal trajectory analysis framework for modeling visitor engagement with an interactive science exhibit for environmental sustainability.
DATE:
TEAM MEMBERS:
Andrew EmersonNathan HendersonWookhee MinJonathan RoweJames MinogueJames Lester
resourceresearchMuseum and Science Center Exhibits
In this paper, we introduce a Bayesian hierarchical modeling framework for predicting learner engagement with Future Worlds, a tabletop science exhibit for environmental sustainability.
DATE:
TEAM MEMBERS:
Andrew EmersonNathan HendersonJonathan RoweWookhee MinSeung LeeJames MinogueJames Lester
resourceresearchMuseum and Science Center Exhibits
In this paper, we investigate bias detection and mitigation techniques to address issues of
algorithmic fairness in multimodal models of museum visitor visual attention.
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TEAM MEMBERS:
Halim AcostaNathan HendersonJonathan RoweWookhee MinJames MinogueJames Lester
resourceresearchMuseum and Science Center Exhibits
Re-Living Paleontology conducted usability and comparison studies on immersion and interactivity in augmented reality (AR) and visitors' engagement and understanding of science. Two "Tar AR" experiences were designed and studied.
DATE:
TEAM MEMBERS:
Benjamin D. NyeWilliam SwartoutDavid NelsonGale M. SinatraEmily LindseyMolly Porter
Overview
In 2020, RK&A (now Kera Collective) partnered with the National Museum of Natural History to conduct a summative evaluation of the David H. Koch Hall of Fossils: Deep Time, a 31,000 square foot exhibition that explores how Earth’s distant past shapes our future.
Our evaluation explored how visitors process complex scientific topics, such as climate change, mass extinctions, the evolution of life on Earth, and humans’ role in positively impacting Earth’s future.
Approach
We designed a large-scale evaluation study with four distinct but interrelated parts:
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